CN104640154A - Non-uniform clustering routing method for intelligent traffic system - Google Patents
Non-uniform clustering routing method for intelligent traffic system Download PDFInfo
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- CN104640154A CN104640154A CN201510084264.4A CN201510084264A CN104640154A CN 104640154 A CN104640154 A CN 104640154A CN 201510084264 A CN201510084264 A CN 201510084264A CN 104640154 A CN104640154 A CN 104640154A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/08—Load balancing or load distribution
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
- H04W40/32—Connectivity information management, e.g. connectivity discovery or connectivity update for defining a routing cluster membership
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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Abstract
The invention discloses a non-uniform clustering routing method for an intelligent traffic system. The method adopts a shuffled frog leaping algorithm, and comprises the following steps that a network model and clustering strategies are built, adjacent-cluster routing parameters are initialized, adjacent-cluster routing is locally and totally optimized, and the cluster radius is dynamically regulated. The non-uniform clustering routing method has the advantages that the survival period of a sensing network can be prolonged, the net convergence time of the work is shortened, and in addition, the load balance of the network is improved.
Description
Technical field
The invention belongs to wireless communication technology field, be specifically related to a kind of method of the Uneven Cluster Routing Protocol for intelligent transportation system.
Background technology
Wireless sensor network (WSNs) all has broad application prospects in fields such as military, medical treatment, agricultural, industry and business application.At present, in intelligent transportation system, fusion based on the multi-source traffic data of wireless sensor network (WSNs) can obtain transport information more accurate than traditional sensor-based system, thus realize more effective traffic administration, as application such as vehicle flowrate monitoring, parking management, crossing traffic induction and energy efficient.
In the application of intelligent transportation wireless sensor network (WSNs) structural design by vehicle between form movement, the Fixed Cellular Network structure that forms between distributed ad-hoc form with highway facilities upper sensor combines.There are two kinds of information communication types, one is called car-car cooperative system (Vehicle-to-Vehicle), namely vehicle is equipped with transducer to carry out the information interaction between vehicle, this is as most important in serious conditions such as traffic jams for being avoided, another kind is called car-road cooperative system (Vehicle-to-Infrastructure), namely carry out information transmission between vehicle and the transducer being arranged on highway fixation means, this timely feedback for traffic conditions on road particularly highway is particularly important.Routing Protocol is particularly crucial for the network transmission performance in traffic information collection.There is due to clustering route protocol the features such as good energy saving property, now become class wireless sensor network (WSNs) Routing Protocol of primary study.In appearing in the newspapers, low power consumption adaptive collection bunch layered protocol (LEACH) is the even clustering route protocol occurred the earliest.But such is cluster structured also brings some problems.Research shows, cluster head random-selection and bunch in single-hop route can increase node energy consumption and limit network size.In addition, in larger wireless sensor network (WSNs), adopt multi-hop communication mode to communicate with aggregation node (Sink) between bunch head, thus cause " hot-zone " problem.The sub-clustering quasi-protocol (as Heed) using fixed tuft radius is the cluster quasi-protocol improved based on low power consumption adaptive collection bunch layered protocol (LEACH), mainly in cluster head election, adds capacity factor and considers.For reaction equation wireless sensor network and the major architectural of routing policy (as TEEN) that designs is consistent with low power consumption adaptive collection bunch layered protocol (LEACH), be that a class reply is pressed for time the response type Routing Protocol of event.Low power consumption adaptive integrates a bunch layered protocol (LEACH), the sub-clustering quasi-protocol (Heed) using fixed tuft radius and the routing policy (TEEN) that designs as reaction equation wireless sensor network does not all consider " hot-zone " problem.The Uneven Cluster agreement (EEUC) of energy efficient is a kind of non-homogeneous cluster Routing Protocol, it constructs ascending bunch radius by near to far away according to the distance of distance aggregation node (Sink), make near aggregation node (Sink) bunch number of members be less than away from aggregation node (Sink) bunch, thus alleviate the consumption of bunch head energy near aggregation node (Sink).But the Uneven Cluster quasi-protocol (as EEUC) of energy efficient is applicable to the more uniform situation of Node distribution.If at the applied environment interior joint skewness of reality, the Uneven Cluster quasi-protocol (EEUC) of energy efficient still cannot alleviate " hot-zone " problem.
In intelligent transportation scene, sensor node is many, and the original data volume of collection is larger.In addition, vehicle node has high mobility, makes the Node distribution situation of vehicle sensors network have change frequently than static sensor network.Therefore, existing layering wireless sensor network (WSNs) Routing Protocol is all not too applicable to intelligent transportation environment.And the Uneven Cluster Routing Protocol (UCSNP) for intelligent transportation system disclosed in this invention there is not been reported.
Summary of the invention
The object of the invention is to overcome above-mentioned shortcoming and provide a kind of improve sensing network life cycle, shorten network convergence time and improve the method for the Uneven Cluster Routing Protocol for intelligent transportation system of the load balancing of network.
The method of a kind of Uneven Cluster Routing Protocol for intelligent transportation system of the present invention, comprises the following steps:
(1) network model and clustering algorithm is set up:
In two dimensional surface region:
, all the sensors node random distribution, formats regional network, and each square area of its grid network is
, the length of side
determine according to the solving precision of application task;
The position of two nodes is respectively
with
, then the distance between two positions is:
This Region dividing is become
individual district,
quantity determined by the length in this region and the communication radius of partitioned nodes.Aggregation node (Sink) node deployment is in an extra-regional fixed position; In order to realize energy consumption balance, the number of clusters order in the subregion that distance aggregation node (Sink) is near should more than subregion far away; Due to the high mobility of vehicle node under traffic environment, bunch head of each bunch is appointed as a fixation means in this bunch of district by Sink node; Single-hop communication between other node and bunch head, bunch head controls number of members to reduce traffic load by adjustment communication radius; For adjacent two bunches bunch heads
with
, then
, wherein
represent
with
between distance,
with
represent corresponding radius; In addition, sensor node not under any one bunch of communication radius covers can adopt the multi-hop communication of chain structure, select the best via node of signal strength signal intensity to transmit perception data based on greedy algorithm, in this way data are passed to the member node in nearest bunch; After this node carries out a data fusion as first-in-chain(FIC), by Packet Generation to bunch head;
Each sensor node in network, there is the mark (ID) that unique, be responsible for entity authentication, registration authorizing by trusted party (Trusted Authority), the mutual identity verification of digital certificate that node is signed and issued by mark (ID) and trusted party (Trusted Authority);
If node is launched
bit data to distance is
position, then the energy of transmitting terminal is:
The energy of receiving terminal is:
。
(2) cluster-level routing parameter initialization:
Bunch district's intra-cluster head
periodically send inquiry message to other member node
with lock in time;
Each takes turns sampling
, bunch head
the perception data received is merged, generates new data packets to aggregation node (Sink); During transmission,
adjacent cluster head can be selected as via node forwarding data bag; Therefore, from source point
one or more different path can be generated between aggregation node (Sink); Except with aggregation node (Sink) hop distance bunch except, bunch head
stochastic generation
frog, every frog individuality represent one from
to the feasible path of aggregation node (Sink), namely
, wherein
represent the dimension of solution space,
,
be the initial population of generation;
Frog ideal adaptation degree function is:
In formula (5),
,
represent
it is path
on a node,
represent
be bunch
interior member node,
represent bunch head
to member node
receive/send the energy that data consume,
represent
be
adjacent cluster head,
represent
right
receive/send the energy that data consume,
represent
the energy that fused data consumes,
represent
carry out the energy that corresponding calculating consumes;
Function
path is described
overall energy consumption, comprise calculation cost and the communication load of each node on this path, each takes turns data sampling, bunch head
receive bunch in member node and the message from adjacent cluster head, calculate each energy consumption parameter and upgrade from this bunch of head to the cost of every paths of aggregation node (Sink), wherein, coefficient
for the proportion of various parameter in node overall energy consumption; Between bunch, optimum path problems equals frog optimal solution problem; Optimal solution is target function
get minimum value, namely
get maximum;
the frog fitness generated is become by descending
, and be divided into
individual population
, constructor population,
numerical value by
the number of the next-hop node of middle source point decides; Every sub-population comprises
frog, meets following relationship:
。
(3) cluster-level routing optimization:
Cluster-level routing Optimization Steps comprises cluster-level routing local optimum step and cluster-level routing global optimization step;
(3.1) cluster-level routing local optimum:
Cluster-level routing local optimum carries out Local Search respectively to the sub-population of frog population dividing;
Cluster-level routing local optimum step 1:
wheel,
, calculate sub-population
fitness
, with probability
upgrade one by one, and find out optimal solution
the poorest solution
;
Cluster-level routing local optimum step 2:
with
carry out intersection replacement operation, namely search the same node point in two footpaths, to next common node pair from the common node chosen
carry out link replacement, if two paths only have a same node point, then get aggregation node (Sink) for next common point;
Cluster-level routing local optimum step 3: calculate the poorest solution after intersecting
fitness
if,
the poorest solution fitness before being greater than replacement
, be then replaced successfully, otherwise, intersect unsuccessfully, if intersect unsuccessfully, then select globally optimal solution again
right
carry out intersection to replace, if
still be not improved, then the frog that generation one is new at random replaces former
;
(3.2) cluster-level routing global optimization:
Cluster-level routing global optimization step 1: after epicycle search terminates, carry out new round Local Search;
Cluster-level routing global optimization step 2: repeat cluster-level routing global optimization step 1, pass through
after wheel local optimum, the solution of all sub-populations is mixed, again by fitness
descending, repartitions a bunch group;
Cluster-level routing global optimization step 3: repeat cluster-level routing global optimization step 2, until meet target function
be worth minimum till.
(4) dynamic conditioning of bunch radius:
Each takes turns sampling
, bunch head
its competition radius can be calculated as follows:
In formula (6),
for a bunch district
interior member node to the ultimate range of aggregation node (Sink) and minimum range,
for a bunch head
the maximum occurrences of competition radius,
for the parameter between 0-1, be used for controlling span;
In bunch, member node and bunch head adopt single-hop communication mode to communicate, the data that member node gathers except sending self, also need to forward from bunch data transmitted by chain structure of other node outer; For avoiding leader cluster node to bear HD, consuming more energy, making bunch head premature failure or a packet discard, thus cause perception cavity, for this reason, Uneven Cluster Routing Protocol (UCSNP) takes a kind of dynamic bunch of radius optimisation strategy with load balancing between knot modification
Weights are set
computing formula is as follows:
In formula (7),
for the parameter between 0-1,
for bunch
interior member node
the energy consumed in epicycle sampling,
for a bunch head
the energy consumed in epicycle sampling,
for a bunch head
at the communication radius of epicycle, can draw from formula (7), weights
decided with the ratio to bunch head communication radius to a bunch head distance summation by the ratio of member node total energy consumption and bunch head energy consumption and member node.
The method of above-mentioned a kind of Uneven Cluster Routing Protocol for intelligent transportation system, wherein: described sensor node comprises vehicle node and fixation means node.
The present invention compared with prior art has obvious beneficial effect, as can be known from the above technical solutions: the method for the Uneven Cluster Routing Protocol that the present invention proposes, the feature of combined with intelligent traffic system, as sensor node causes the original data volume of collection larger more, the high mobility that vehicle node has makes the Node distribution situation of vehicle sensors network have frequently change etc. than static sensor network, set up the communication mode of chain structure and cluster structured mixing, and under this communication mode, by shuffled frog leaping algorithm, cluster-level routing is optimized, dynamic conditioning bunch communication radius, Uneven Cluster Routing Protocol (UCSNP) makes network topology more reasonable, improves the life cycle of network and has shortened the convergence time of network, effectively can solve " hot-zone " problem that the transport information based on wireless sensor network (WSNs) is transmitted.In addition, the introducing of Dynamic Cluster radius optimisation strategy can load balancing between knot modification, avoids the unbalanced drawback of centralized clustering mechanism offered load.
Accompanying drawing explanation
Fig. 1 is Uneven Cluster agreement (EEUC) the energy consumption comparison diagram of Uneven Cluster Routing Protocol (UCSNP) and energy efficient.
Fig. 2 is Uneven Cluster agreement (EEUC) the surviving node number comparison diagram of Uneven Cluster Routing Protocol (UCSNP) and energy efficient.
Fig. 3 is Uneven Cluster agreement (EEUC) the convergence in mean time comparison diagram of Uneven Cluster Routing Protocol (UCSNP) and energy efficient.
Fig. 4 is the network configuration of chain type and sub-clustering mixing.
Embodiment
The method of a kind of Uneven Cluster Routing Protocol (UCSNP) for intelligent transportation system of the present invention, adopts shuffled frog leaping algorithm, comprises the following steps:
(1) network model and clustering algorithm is set up:
In two dimensional surface region:
, all the sensors node random distribution, formats regional network, and each square area of its grid network is
, the length of side
determine according to the solving precision of application task;
The position of two nodes is respectively
with
, then the distance between two positions is:
This Region dividing is become
individual district,
quantity determined by the length in this region and the communication radius of partitioned nodes.Aggregation node (Sink) node deployment is in an extra-regional fixed position; In order to realize energy consumption balance, the number of clusters order in the subregion that distance aggregation node (Sink) is near should more than subregion far away; Due to the high mobility of vehicle node under traffic environment, bunch head of each bunch is appointed as a fixation means in this bunch of district by Sink node; Single-hop communication between other node and bunch head, bunch head controls number of members to reduce traffic load by adjustment communication radius; For adjacent two bunches bunch heads
with
, then
, wherein
represent
with
between distance,
with
represent corresponding radius; In addition, sensor node not under any one bunch of communication radius covers can adopt the multi-hop communication of chain structure, select the best via node of signal strength signal intensity to transmit perception data based on greedy algorithm, in this way data are passed to the member node in nearest bunch; After this node carries out a data fusion as first-in-chain(FIC), by Packet Generation to bunch head, as shown in Figure 4;
Each sensor node in network, there is the mark (ID) that unique, be responsible for entity authentication, registration authorizing by trusted party (Trusted Authority), the mutual identity verification of digital certificate that node is signed and issued by mark (ID) and trusted party (Trusted Authority).
If node is launched
bit data to distance is
position, then the energy of transmitting terminal is:
The energy of receiving terminal is:
。
(2) cluster-level routing parameter initialization:
Bunch district's intra-cluster head
periodically send inquiry message to other member node
with lock in time;
Each takes turns sampling
, bunch head
the perception data received is merged, generates new data packets to aggregation node (Sink); During transmission,
adjacent cluster head can be selected as via node forwarding data bag; Therefore, from source point
one or more different path can be generated between aggregation node (Sink); Except with aggregation node (Sink) hop distance bunch except, bunch head
stochastic generation
frog, every frog individuality represent one from
to the feasible path of aggregation node (Sink), namely
, wherein
represent the dimension of solution space,
,
be the initial population of generation;
Frog ideal adaptation degree function is:
In formula (5),
,
represent
it is path
on a node,
represent
be bunch
interior member node,
represent bunch head
to member node
receive/send the energy that data consume,
represent
be
adjacent cluster head,
represent
right
receive/send the energy that data consume,
represent
the energy that fused data consumes,
represent
carry out the energy that corresponding calculating consumes;
Function
path is described
overall energy consumption, comprise calculation cost and the communication load of each node on this path, each takes turns data sampling, bunch head
receive bunch in member node and the message from adjacent cluster head, calculate each energy consumption parameter and upgrade from this bunch of head to the cost of every paths of aggregation node (Sink), wherein, coefficient
for the proportion of various parameter in node overall energy consumption; Between bunch, optimum path problems equals frog optimal solution problem; Optimal solution is target function
get minimum value, namely
get maximum;
the frog fitness generated is become by descending
, and be divided into
individual population
, constructor population,
numerical value by
the number of the next-hop node of middle source point decides; Every sub-population comprises
frog, meets following relationship:
。
(3) cluster-level routing optimization:
Cluster-level routing Optimization Steps comprises cluster-level routing local optimum step and cluster-level routing global optimization step;
(3.1) cluster-level routing local optimum:
Cluster-level routing local optimum carries out Local Search respectively to the sub-population of frog population dividing;
Cluster-level routing local optimum step 1:
wheel,
, calculate sub-population
fitness
, with probability
upgrade one by one, and find out optimal solution
the poorest solution
;
Cluster-level routing local optimum step 2:
with
carry out intersection replacement operation, namely search the same node point in two footpaths, to next common node pair from the common node chosen
carry out link replacement, if two paths only have a same node point, then get aggregation node (Sink) for next common point;
Cluster-level routing local optimum step 3: calculate the poorest solution after intersecting
fitness
if,
the poorest solution fitness before being greater than replacement
, be then replaced successfully, otherwise, intersect unsuccessfully, if intersect unsuccessfully, then select globally optimal solution again
right
carry out intersection to replace, if
still be not improved, then the frog that generation one is new at random replaces former
;
(3.2) cluster-level routing global optimization:
Cluster-level routing global optimization step 1: after epicycle search terminates, carry out new round Local Search;
Cluster-level routing global optimization step 2: repeat cluster-level routing global optimization step 1, pass through
after wheel local optimum, the solution of all sub-populations is mixed, again by fitness
descending, repartitions a bunch group;
Cluster-level routing global optimization step 3: repeat cluster-level routing global optimization step 2, until meet target function
be worth minimum till.
(4) dynamic conditioning of bunch radius:
Each takes turns sampling
, bunch head
its competition radius can be calculated as follows:
In formula (6),
for a bunch district
interior member node to the ultimate range of aggregation node (Sink) and minimum range,
for a bunch head
the maximum occurrences of competition radius,
for the parameter between 0-1, be used for controlling span;
In bunch, member node and bunch head adopt single-hop communication mode to communicate, the data that member node gathers except sending self, also need to forward from bunch data transmitted by chain structure of other node outer; For avoiding leader cluster node to bear HD, consuming more energy, making bunch head premature failure or a packet discard, thus cause perception cavity, for this reason, Uneven Cluster Routing Protocol (UCSNP) takes a kind of dynamic bunch of radius optimisation strategy with load balancing between knot modification
Weights are set
computing formula is as follows:
In formula (7),
for the parameter between 0-1,
for bunch
interior member node
the energy consumed in epicycle sampling,
for a bunch head
the energy consumed in epicycle sampling,
for a bunch head
at the communication radius of epicycle, can draw from formula (7), weights
decided with the ratio to bunch head communication radius to a bunch head distance summation by the ratio of member node total energy consumption and bunch head energy consumption and member node.
Described sensor node comprises vehicle node and fixation means node.
experimental example:
Below by communication and the node energy consumption of artificial network interior joint, mainly from energy consumption and convergence time aspect, the Uneven Cluster agreement (EEUC) of Uneven Cluster Routing Protocol (UCSNP) with energy efficient is analyzed, further illustrates beneficial effect of the present invention.
The monitored area of a 200m X 100m is set, form 1 aggregation node (Sink) and several bunches by cluster algorithm in region, have 1 aggregators in each bunch, wherein, aggregation node (Sink) and aggregators are fixed position, and all the other nodes become random distribution.The total number of node is 100, and the maximum communication radius of ordinary node is 10m, and the maximum communication radius of bunch head is 20m.Ordinary node primary power is 1J, and aggregation node (Sink) and aggregators are then 40J, and the maximum of packet is 128B, and inter-node transmission bandwidth is 0.5Mbps.Arrange node periodically perception and transmission data, the sampling period of each node is 0.5-1.0s, and the cycle sending data is 0.2-0.5s, and frog colony sum maximum is 50, and the iterations of global optimization is N=10, and each emulation duration is 300s.
Experiment is altogether carried out 300 and is taken turns.
The energy consumption of the Uneven Cluster agreement (EEUC) of contrast Uneven Cluster Routing Protocol (UCSNP) and energy efficient as shown in Figure 1, as can be seen from Figure 1, Uneven Cluster agreement (EEUC) network partition of Uneven Cluster Routing Protocol (UCSNP) and energy efficient is reasonable, energy consumption curve is all more steady, namely, in identical simulation time, energy consumption consumes balanced.Uneven Cluster Routing Protocol (UCSNP) is owing to introducing a bunch radius optimisation strategy, and make there is more bunch scale in network close to optimum, the Uneven Cluster agreement (EEUC) comparing energy efficient decreases energy consumption.Bunch head and aggregation node (Sink) are all roadside fixation means, thus the impact of its energy consumption problem to compare ordinary node much smaller.
The surviving node number of the Uneven Cluster agreement (EEUC) of contrast Uneven Cluster Routing Protocol (UCSNP) and energy efficient as shown in Figure 2, as can be seen from Figure 2, the death wheel number of Uneven Cluster agreement (EEUC) first node of energy efficient the 88th to be taken turns, and the death wheel number of non-homogeneous clustering route protocol (UCSNP) first node 123 to be taken turns, the efficient Uneven Cluster agreement (EEUC) of specific energy has been postponed 35 and has been taken turns.When the 300th takes turns, Uneven Cluster agreement (EEUC) the surviving node number of energy efficient is 29, and the surviving node number of non-homogeneous clustering route protocol (UCSNP) is 57.Draw from above analysis, Uneven Cluster Routing Protocol (UCSNP) owing to introducing the cluster-level routing optimization based on shuffled frog leaping algorithm, thus extends network lifecycle.
Uneven Cluster agreement (EEUC) the end-to-end convergence in mean time of contrast Uneven Cluster Routing Protocol (UCSNP) and energy efficient as shown in Figure 3, as can be seen from Figure 3, when network topology changes, the Uneven Cluster agreement (EEUC) of energy efficient bunch in re-elect a bunch head, and by bunch between consult to set up bunch communication radius.When network size is larger, this agreement makes performance decline rapidly due to increasing considerably of convergence time.But, in Uneven Cluster Routing Protocol (UCSNP), by fixed cluster heads
or its spare cluster head
re-establish a bunch communication, without the need to election bunch head.In addition, some member node connects from bunch outer chain structure to share the load of bunch head.When between two bunches of heads, hop count increases, the Uneven Cluster agreement (EEUC) of energy efficient is similar to linear change, and non-homogeneous clustering route protocol (UCSNP) is in concave curve.Visible, compare the Uneven Cluster agreement (EEUC) of energy efficient, Uneven Cluster Routing Protocol (UCSNP) obviously reduces in convergence time.
The above, it is only preferred embodiment of the present invention, not any pro forma restriction is done to the present invention, anyly do not depart from technical solution of the present invention content, the any simple modification done above embodiment according to technical spirit of the present invention, equivalent variations and modification, all still belong in the scope of technical solution of the present invention.
Claims (2)
1., for a method for the Uneven Cluster Routing Protocol of intelligent transportation system, comprise the following steps:
(1) network model and clustering algorithm is set up:
In two dimensional surface region:
, all the sensors node random distribution, formats regional network, and each square area of its grid network is
, the length of side
determine according to the solving precision of application task;
The position of two nodes is respectively
with
, then the distance between two positions is:
This Region dividing is become
individual district,
quantity determined by the length in this region and the communication radius of partitioned nodes, aggregation node (Sink) node deployment is in an extra-regional fixed position; In order to realize energy consumption balance, the number of clusters order in the subregion that distance aggregation node (Sink) is near should more than subregion far away; Due to the high mobility of vehicle node under traffic environment, bunch head of each bunch is appointed as a fixation means in this bunch of district by Sink node; Single-hop communication between other node and bunch head, bunch head controls number of members to reduce traffic load by adjustment communication radius; For adjacent two bunches bunch heads
with
, then
, wherein
represent
with
between distance,
with
represent corresponding radius; In addition, sensor node not under any one bunch of communication radius covers can adopt the multi-hop communication of chain structure, select the best via node of signal strength signal intensity to transmit perception data based on greedy algorithm, in this way data are passed to the member node in nearest bunch; After this node carries out a data fusion as first-in-chain(FIC), by Packet Generation to bunch head;
Each sensor node in network, there is the mark (ID) that unique, be responsible for entity authentication, registration authorizing by trusted party (Trusted Authority), the mutual identity verification of digital certificate that node is signed and issued by mark (ID) and trusted party (Trusted Authority);
If node is launched
bit data to distance is
position, then the energy of transmitting terminal is:
The energy of receiving terminal is:
(2) cluster-level routing parameter initialization:
Bunch district's intra-cluster head
periodically send inquiry message to other member node
with lock in time;
Each takes turns sampling
, bunch head
the perception data received is merged, generates new data packets to aggregation node (Sink); During transmission,
adjacent cluster head can be selected as via node forwarding data bag; Therefore, from source point
one or more different path can be generated between aggregation node (Sink); Except with aggregation node (Sink) hop distance bunch except, bunch head
stochastic generation
frog, every frog individuality represent one from
to the feasible path of aggregation node (Sink), namely
, wherein
represent the dimension of solution space,
,
be the initial population of generation;
Frog ideal adaptation degree function is:
In formula (5),
,
represent
it is path
on a node,
represent
be bunch
interior member node,
represent bunch head
to member node
receive/send the energy that data consume,
represent
be
adjacent cluster head,
represent
right
receive/send the energy that data consume,
represent
the energy that fused data consumes,
represent
carry out the energy that corresponding calculating consumes;
Function
path is described
overall energy consumption, comprise calculation cost and the communication load of each node on this path, each takes turns data sampling, bunch head
receive bunch in member node and the message from adjacent cluster head, calculate each energy consumption parameter and upgrade from this bunch of head to the cost of every paths of aggregation node (Sink), wherein, coefficient
for the proportion of various parameter in node overall energy consumption; Between bunch, optimum path problems equals frog optimal solution problem; Optimal solution is target function
get minimum value, namely
get maximum;
the frog fitness generated is become by descending
, and be divided into
individual population
, constructor population,
numerical value by
the number of the next-hop node of middle source point decides; Every sub-population comprises
frog, meets following relationship:
(3) cluster-level routing optimization:
Cluster-level routing Optimization Steps comprises cluster-level routing local optimum step and cluster-level routing global optimization step;
(3.1) cluster-level routing local optimum:
Cluster-level routing local optimum carries out Local Search respectively to the sub-population of frog population dividing;
Cluster-level routing local optimum step 1:
wheel,
, calculate sub-population
fitness
, with probability
upgrade one by one, and find out optimal solution
the poorest solution
;
Cluster-level routing local optimum step 2:
with
carry out intersection replacement operation, namely search the same node point in two footpaths, to next common node pair from the common node chosen
carry out link replacement, if two paths only have a same node point, then get aggregation node (Sink) for next common point;
Cluster-level routing local optimum step 3: calculate the poorest solution after intersecting
fitness
if,
the poorest solution fitness before being greater than replacement
, be then replaced successfully, otherwise, intersect unsuccessfully, if intersect unsuccessfully, then select globally optimal solution again
right
carry out intersection to replace, if
still be not improved, then the frog that generation one is new at random replaces former
;
(3.2) cluster-level routing global optimization:
Cluster-level routing global optimization step 1: after epicycle search terminates, carry out new round Local Search;
Cluster-level routing global optimization step 2: repeat cluster-level routing global optimization step 1, pass through
after wheel local optimum, the solution of all sub-populations is mixed, again by fitness
descending, repartitions a bunch group;
Cluster-level routing global optimization step 3: repeat cluster-level routing global optimization step 2, until meet target function
be worth minimum till;
(4) dynamic conditioning of bunch radius:
Each takes turns sampling
, bunch head
its competition radius can be calculated as follows:
In formula (6),
for a bunch district
interior member node to the ultimate range of aggregation node (Sink) and minimum range,
for a bunch head
the maximum occurrences of competition radius,
for the parameter between 0-1, be used for controlling span;
In bunch, member node and bunch head adopt single-hop communication mode to communicate, the data that member node gathers except sending self, also need to forward from bunch data transmitted by chain structure of other node outer; For avoiding leader cluster node to bear HD, consuming more energy, making bunch head premature failure or a packet discard, thus cause perception cavity, for this reason, Uneven Cluster Routing Protocol (UCSNP) takes a kind of dynamic bunch of radius optimisation strategy with load balancing between knot modification
Weights are set
computing formula is as follows:
In formula (7),
for the parameter between 0-1,
for bunch
interior member node
the energy consumed in epicycle sampling,
for a bunch head
the energy consumed in epicycle sampling,
for a bunch head
at the communication radius of epicycle, can draw from formula (7), weights
decided with the ratio to bunch head communication radius to a bunch head distance summation by the ratio of member node total energy consumption and bunch head energy consumption and member node.
2. the method for a kind of Uneven Cluster Routing Protocol for intelligent transportation system as claimed in claim 1, wherein: described sensor node comprises vehicle node and fixation means node.
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